.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_linear_model_plot_omp.py: =========================== Orthogonal Matching Pursuit =========================== Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encoded with a dictionary .. image:: /auto_examples/linear_model/images/sphx_glr_plot_omp_001.png :class: sphx-glr-single-img .. code-block:: default print(__doc__) import matplotlib.pyplot as plt import numpy as np from sklearn.linear_model import OrthogonalMatchingPursuit from sklearn.linear_model import OrthogonalMatchingPursuitCV from sklearn.datasets import make_sparse_coded_signal n_components, n_features = 512, 100 n_nonzero_coefs = 17 # generate the data # y = Xw # |x|_0 = n_nonzero_coefs y, X, w = make_sparse_coded_signal(n_samples=1, n_components=n_components, n_features=n_features, n_nonzero_coefs=n_nonzero_coefs, random_state=0) idx, = w.nonzero() # distort the clean signal y_noisy = y + 0.05 * np.random.randn(len(y)) # plot the sparse signal plt.figure(figsize=(7, 7)) plt.subplot(4, 1, 1) plt.xlim(0, 512) plt.title("Sparse signal") plt.stem(idx, w[idx], use_line_collection=True) # plot the noise-free reconstruction omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs) omp.fit(X, y) coef = omp.coef_ idx_r, = coef.nonzero() plt.subplot(4, 1, 2) plt.xlim(0, 512) plt.title("Recovered signal from noise-free measurements") plt.stem(idx_r, coef[idx_r], use_line_collection=True) # plot the noisy reconstruction omp.fit(X, y_noisy) coef = omp.coef_ idx_r, = coef.nonzero() plt.subplot(4, 1, 3) plt.xlim(0, 512) plt.title("Recovered signal from noisy measurements") plt.stem(idx_r, coef[idx_r], use_line_collection=True) # plot the noisy reconstruction with number of non-zeros set by CV omp_cv = OrthogonalMatchingPursuitCV() omp_cv.fit(X, y_noisy) coef = omp_cv.coef_ idx_r, = coef.nonzero() plt.subplot(4, 1, 4) plt.xlim(0, 512) plt.title("Recovered signal from noisy measurements with CV") plt.stem(idx_r, coef[idx_r], use_line_collection=True) plt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38) plt.suptitle('Sparse signal recovery with Orthogonal Matching Pursuit', fontsize=16) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.686 seconds) **Estimated memory usage:** 8 MB .. _sphx_glr_download_auto_examples_linear_model_plot_omp.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_omp.ipynb :width: 150 px .. container:: sphx-glr-download :download:`Download Python source code: plot_omp.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_omp.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_